A Novel Greedy Randomized Dynamic Ensemble Selection Algorithm

Rui, Yong; Dai, Qun

doi:10.1007/s11063-017-9670-y

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…A more successful and widely used approach though is to use a group of different classifiers (ensemble) to cope with changes in the environment. Several different ensemble models have been proposed in the literature, including recent approaches like [56][57][58], and may or may not weigh each of its members. Most models using weighted classifier ensembles determine the weights for each classifier using a set of heuristics related to classifier performance in the most recent data received [22].…”

Section: Introductionmentioning

confidence: 99%

Neuroevolutionary learning in nonstationary environments

Escovedo

Koshiyama

Cruz³

et al. 2020

Appl Intell

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This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantuminspired evolutionary models to automatically configure the ensemble members and combine their output. The quantuminspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour.

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